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Dirac Mixture Approximation of Multivariate Gaussian Densities

Hanebeck, Uwe D.; Huber, Marco F.; Klumpp, Vesa


For the optimal approximation of multivariate Gaussian densities by means of Dirac mixtures, i.e., by means of a sum of weighted Dirac distributions on a continuous domain, a novel systematic method is introduced. The parameters of this approximate density are calculated by minimizing a global distance measure, a generalization of the well-known Cram\'{e}r- von Mises distance to the multivariate case. This generalization is obtained by defining an alternative to the classical cumulative distribution, the Localized Cumulative Distribution (LCD). In contrast to the cumulative distribution, the LCD is unique and symmetric even in the multivariate case. The resulting deterministic approximation of Gaussian densities by means of discrete samples provides the basis for new types of Gaussian filters for estimating the state of nonlinear dynamic systems from noisy measurements.

Volltext §
DOI: 10.5445/IR/1000034950
DOI: 10.1109/CDC.2009.5400649
Zitationen: 33
Cover der Publikation
Zugehörige Institution(en) am KIT Fakultät für Informatik – Institut für Anthropomatik (IFA)
Publikationstyp Proceedingsbeitrag
Publikationsjahr 2009
Sprache Englisch
Identifikator ISBN: 978-1-4244-3872-3
KITopen-ID: 1000034950
Erschienen in Proceedings of the 48th IEEE Conference on Decision and Control (CDC). Shanghai, China, 15 - 18 December 2009. T. 7
Verlag Institute of Electrical and Electronics Engineers (IEEE)
Seiten 3851-3858
Nachgewiesen in Dimensions
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